Hyperspectral image processing for target detection using Spectral Angle Mapping

In this paper we concentrate on understanding the Hyperspectral Image subspace, spectral processing of the Hyperspectral Image using Spectral Angle Mapping to achieve target detection. A combined spatial-spectral integrated processing algorithm is proposed to be implemented in cases where spectral processing produces probable target pixels that are spatially spread. Atmospheric error correction is done using the method of Internal Average Relative Reflectance. To reduce processing time necessary dimensionality reduction has been implemented using Principal Component Analysis. EO-1 Hyperion datasets have been used for this project. The results of both the spectral classification and the proposed integrated spatial-spectral processing algorithm with and without atmospheric error correction as well as with and without dimensionality reduction has been analysed using ENVI Image processing toolbox as well as using MATLAB. The effectiveness of each method and the difference in results using different platforms has been inferred from the numerical experiments.